Instructions to use bergum/product_title_encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bergum/product_title_encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bergum/product_title_encoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("bergum/product_title_encoder") model = AutoModel.from_pretrained("bergum/product_title_encoder") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c94424de19c71e7c96f211e47865d30a457b26fd5f9dc454d67b8342d01d04d6
- Size of remote file:
- 90.9 MB
- SHA256:
- fdfc7d567fa56bc5cd94515eb32fb153180b27d00515165ab4ceb03e241b95d9
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